Dv Overview

Skill: Overview — What to Use and When

This skill provides cross-cutting context that no individual skill owns: tool capabilities, UX principles, and the skill index. Per-task routing is handled by each skill's WHEN/DO NOT USE WHEN frontmatter triggers — not duplicated here.

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Hard Rules — Read These First

These rules are non-negotiable. Violating any of them means the task is going off-rails.

0. Check Init State Before Anything Else

Before writing ANY code or creating ANY files, check if the workspace is initialized:

ls .env scripts/auth.py 2>/dev/null

• If BOTH exist: workspace is initialized. Proceed to the relevant task.
• If EITHER is missing: Automatically run the connect flow (see the dv-connect skill). Do NOT ask the user whether to initialize — just do it. Do not create your own .env, requirements.txt, .env.example, or auth scripts. The dv-connect skill handles all of this.

Do NOT create requirements.txt, .env.example, or scaffold files manually. The connect flow produces the correct file structure. Skipping it is the #1 cause of broken setups.

1. Python Only — No Exceptions

All scripts, data operations, and automation MUST use Python. This plugin's entire toolchain — scripts/auth.py, the Dataverse SDK, all skill examples — is Python-based.

NEVER:

• Run npm init, npm install, or any Node.js/JavaScript tooling
• Install packages via npm, yarn, or pnpm
• Write scripts in JavaScript, TypeScript, PowerShell, or any language other than Python
• Use @azure/msal-node, @azure/identity, or any Node.js Azure SDK
• Import or reference node_modules/

ALWAYS:

• Use pip install for Python packages
• Use scripts/auth.py for authentication tokens and credentials
• Use the Python Dataverse SDK (PowerPlatform-Dataverse-Client) for data and schema operations
• Use azure-identity (Python) for Azure credential flows

If you find yourself about to run npm or create a package.json, STOP. You are going off-rails. Re-read Hard Rule 1 above.

2. MCP → SDK → Web API (in that order)

Before writing ANY code, ask: can MCP handle this? If MCP tools are available in your tool list (list_tables, describe_table, read_query, create_record, etc.):

• Writes: ≤10 records → use MCP directly. 10+ records → use Python SDK (dv-data).
• Reads: Simple filter, small result set (no paging needed) → use MCP. Multi-page iteration, DataFrame loading, aggregation, or queries hitting SQL limits (DISTINCT, HAVING, subqueries) → use Python SDK (dv-query).

Examples where MCP is sufficient: "how many accounts have 'jeff' in the name?", "show me the columns on the contact table", "create an account named Contoso."

If MCP can't handle it (bulk operations, large reads, schema creation, multi-step workflows, analytics, or MCP tools aren't available), use the Python SDK — not raw HTTP. This is the most common mistake agents make.

SDK checklist — evaluate EVERY time you write a script:

• Creating/updating/deleting records? → client.records.create(), .update(), .delete() — see dv-data
• Bulk record operations? → client.records.create(table, [list_of_dicts]) — see dv-data
• Querying or filtering records? → client.records.get(table, select=[...], filter="...") — see dv-query
• Aggregation (top-N, sum, count by group, "most/least")? → Single-table: $apply server-side aggregation (raw Web API). Cross-table: client.dataframe.get() with $select + pd.merge() — see dv-query. Do NOT load all records without $select and aggregate in Python.
• Loading data into pandas? → client.dataframe.get(table) — see dv-query
• Single record by GUID? → client.records.get(table, guid) — see dv-query
• Creating tables, columns, relationships? → client.tables.create(), .add_columns(), .create_lookup_field() — see dv-metadata
• Creating publishers or solutions? → client.records.create("publisher", {...}), client.records.create("solution", {...}) — see dv-solution

Before using from auth import get_token or import requests: check whether the operation is in the Raw Web API list below. If it is not in that list — the SDK supports it — use from auth import get_client instead. Using raw HTTP for SDK-supported operations is the most common off-rails mistake.

Raw Web API (get_token()) is ONLY acceptable for: forms, views, global option sets, N:N $ref associations, N:N $expand, $apply aggregation, memo columns, and unbound actions. Everything else MUST use MCP (if available) or the SDK.

NEVER use raw Web API (get_token() / urllib / requests) for:

• Record CRUD or bulk operations — use client.records.create(), .update(), .delete() (see dv-data)
• Publisher or solution creation — use client.records.create("publisher", {...}) (see dv-solution)
• Table, column, or relationship creation — use client.tables.create(), .add_columns(), .create_lookup_field() (see dv-metadata)
• Querying records — use client.records.get() or client.query.builder() (see dv-query)

If an SDK method fails or a PAC CLI command doesn't exist, consult the relevant skill before falling back to raw HTTP. Improvising raw Web API calls is the #1 cause of off-rails behavior.

Field casing: $select/$filter use lowercase logical names (new_name). $expand and @odata.bind use Navigation Property Names that are case-sensitive and must match $metadata (e.g., new_AccountId). Getting this wrong causes 400 errors. SDK record payloads: pre-b6 the SDK accidentally lowercased @odata.bind keys; b6+ no longer does — but you must still provide the correct SchemaName casing (e.g., new_AccountId@odata.bind). The SDK does not auto-correct wrong casing. Raw Web API calls (forms, views, metadata): casing is entirely manual — a lowercase new_accountid@odata.bind will 400.

Publisher prefix: Never hardcode a prefix (especially not new). Always query existing publishers in the environment and ask the user which to use. The prefix is permanent on every component created with it. See the solution skill's publisher discovery flow.

3. Use Documented Auth Patterns

Authentication is handled by pac auth create (for PAC CLI) and scripts/auth.py (for Python scripts and the SDK).

NEVER:

• Read or parse raw token cache files (e.g., tokencache_msalv3.dat)
• Implement your own MSAL device-code flow
• Hard-code tokens or credentials in scripts
• Invent a new auth mechanism

If auth is expired or missing, re-run pac auth create or check .env credentials. See the dv-connect skill.

4. Follow Skill Instructions, Don't Improvise

Each skill documents a specific, tested sequence of steps. Follow them. If a skill says "use the Python SDK," use the Python SDK — do not substitute a raw HTTP call, a different library, or a different language. If a skill says "run this command," run that command — do not invent an alternative.

Do NOT introspect the SDK with dir(), inspect.signature(), or help() to discover APIs. The skills document the exact methods and parameters to use. If a method call fails with AttributeError, the installed SDK version may not have it — check the version note in the skill and fall back to the documented alternative. Introspecting the SDK wastes time and leads to using deprecated or internal APIs.

If you hit a gap (something the skills don't cover), say so honestly and suggest a workaround. Do not hallucinate a path or improvise a solution using tools the skills don't mention.

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UX Principle: Natural Language First

Users should never need to invoke skills or slash commands directly. The intended workflow is:

1. Install the plugin
2. Describe what you want in plain English
3. The agent figures out the right sequence of tools, APIs, and scripts

Example prompt: "I want to create an extension called IronHandle for Dynamics CRM in this Git repo folder that adds a 'nickname' column to the account table and populates it with a clever nickname every time a new account is created."

From that single prompt, the agent should orchestrate the full sequence: check if the workspace is initialized → create metadata via Web API → write and deploy a C# plugin → pull the solution to the repo. No skill names, no commands — just intent.

Skills exist as the agent's knowledge, not as user-facing commands. Each skill documents how to do one thing well. The agent chains them together based on what the user describes. If a capability gap exists (e.g., prompt columns aren't programmatically creatable yet), say so honestly and suggest workarounds rather than hallucinating a path.

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Multi-Environment Rule (MANDATORY)

Pro-dev scenarios involve multiple environments (dev, test, staging, prod) and multiple sets of credentials. Never assume the active PAC auth profile, values in .env, or anything from memory or a previous session reflects the correct target for the current task.

Before the FIRST operation that touches a specific environment — creating a table, deploying a plugin, pushing a solution, inserting data — you MUST:

1. Show the user the environment URL you intend to use
2. Ask them to confirm it is correct
3. Run pac org who to verify the active connection matches

> "I'm about to make changes to ``. Is this the correct target environment?"

Do not proceed until the user explicitly confirms. This is the single most important safety check in the plugin. Skipping it risks making irreversible changes to the wrong environment.

Once confirmed for a session, you do not need to re-confirm for every subsequent operation in the same session against the same environment.

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What This Plugin Covers

This plugin covers Dataverse / Power Platform development: solutions, tables, columns, forms, views, and data operations (CRUD, bulk, analytics).

It does not cover:

• Power Automate flows (use the maker portal or Power Automate Management API)
• Canvas apps (use pac canvas or the maker portal)
• Azure infrastructure beyond what's needed for service principal setup
• Business Central or other Dynamics products

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Tool Capabilities — Which Tool for Which Job

Understanding the real limits of each tool prevents hallucinated paths. This is the one piece of context no individual skill owns.

| Tool | Use for | Does NOT support | | --- | --- | --- | | MCP Server | Data CRUD (create/read/update/delete records), table create/update/delete/list/describe, column add via update_table, keyword search, single-record fetch | Forms, Views, Relationships, Option Sets, Solutions. Note: table creation may timeout but still succeed — always describe_table before retrying. Run queries sequentially (parallel calls timeout). Column names with spaces normalize to underscores (e.g., "Specialty Area" → cr9ac_specialty_area). SQL limitations: The read_query tool uses Dataverse SQL, which does NOT support: DISTINCT, HAVING, subqueries, OFFSET, UNION, CASE/IF, CAST/CONVERT, or date functions. For analytical queries that need these (e.g., finding duplicates, unmatched records, filtered aggregates), use $apply (single-table aggregation) or client.dataframe.get() with pandas (cross-table) — see dv-query. Bulk operations: MCP create_record creates one record at a time. For 10+ records, use the Python SDK CreateMultiple instead — see dv-data. | | Python SDK (dv-data) | Preferred for all scripted data writes. Record CRUD, upsert (alternate keys), bulk create/update/upsert (CreateMultiple/UpdateMultiple/UpsertMultiple), CSV import with lookup resolution, file column uploads (chunked >128MB) | Forms, Views, global Option Sets, record association ($ref), $apply aggregation, N:N $expand, table/column/relationship creation (use dv-metadata), custom action invocation | | Python SDK (dv-query) | Preferred for bulk reads and analytics. Multi-page record iteration, OData queries (select/filter/expand/orderby), QueryBuilder fluent API (b8+), GUID-free display (formatted values), $expand to resolve lookups, pandas DataFrame handoff (client.dataframe.get()) for cross-table joins and exports, Jupyter notebook snippets | $apply aggregation (use Web API), N:N $expand (use Web API) | | Web API | Everything — forms, views, relationships, option sets, columns, table definitions, unbound actions, $ref association | Nothing (full MetadataService + OData access) | | PAC CLI | Solution export/import/pack/unpack, environment create/list/delete/reset, auth profile management, plugin updates (pac plugin push — first-time registration requires Web API), user/role assignment (pac admin assign-user), solution component management | Data CRUD, metadata creation (tables/columns/forms) | | Azure CLI | App registrations, service principals, credential management | Dataverse-specific operations | | GitHub CLI | Repo management, GitHub secrets, Actions workflow status | Dataverse-specific operations |

Tool priority (always follow this order): MCP for simple reads/queries (small result set, no paging) and ≤10 record writes → Python SDK for bulk reads, scripted writes, bulk operations, and analysis → Web API for operations the SDK doesn't cover (forms, views, option sets, $apply, N:N $expand) → PAC CLI for solution lifecycle. Schema creation (tables/columns/relationships) → SDK via dv-metadata. MCP tools not in your tool list? → Load dv-connect to set them up (see below).

Volume guidance — writes: MCP create_record for 1-10 records. For 10+ records, use dv-data (client.records.create(table, list_of_dicts)) — it uses CreateMultiple internally. Note: the SDK does not chunk automatically; for large datasets, chunk in your script starting at 1,000 and adapt up or down based on success (see dv-data for the adaptive pattern).

Volume guidance — reads: MCP read_query for simple filters and small result sets (no paging needed). For bulk reads (multi-page iteration, all-records loads, DataFrame handoff), use dv-query SDK — it streams pages automatically and avoids MCP SQL limitations. For aggregation queries ($apply), use the Web API directly (see dv-query).

Note: The Python SDK is in preview — breaking changes possible.

MCP Availability Check

If the user's request involves MCP — either explicitly ("connect via MCP", "use MCP", "query via MCP") or implicitly (conversational data queries where MCP would be the natural tool) — check whether Dataverse MCP tools are available in your current tool list (e.g., list_tables, describe_table, read_query, create_record).

If MCP tools are NOT available and the user explicitly asked for MCP (e.g., "use MCP to query", "why isn't MCP working"):

1. Do NOT silently fall back to the Python SDK or Web API
2. Tell the user: "Dataverse MCP tools aren't configured in this session yet."
3. Load the dv-connect skill to set up the MCP server
4. After MCP is configured, stop here — the session must be restarted for MCP tools to appear. Remind the user to resume the session without losing context (Claude Code: claude --continue; Cursor: reload the window with Ctrl+Shift+P → "Developer: Reload Window"; Copilot: reopen the Copilot panel). Do not proceed with SDK. Wait for the user to restart.

If MCP tools are NOT available and the user asked a data question without explicitly requesting MCP (e.g., "how many accounts with 'jeff'?", "show me open tickets"):

1. This is a SDK fallback case — use the Python SDK to answer the question. Do not block the user.
2. After answering, offer: "MCP would handle this conversationally — want me to set it up?"

The distinction matters: explicit MCP request → block and set up MCP. Implicit/conversational question → answer with SDK, offer MCP setup.

If MCP tools ARE available, prefer MCP for simple reads

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Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: microsoft
• Source: microsoft/Dataverse-skills
• License: MIT

Install and usage instructions live in the source repository linked above.